Inspector Roofing Research
A public-safe research framework for understanding how homeowners, search engines, and AI-assisted answer systems describe local roofing questions.
Inspector Roofing and Restoration created the AI Query Intelligence framework to organize privacy-safe local roofing questions into clearer education themes. The project studies the gap between how a homeowner asks a question, such as who to trust for a roof inspection, and the search-style language an AI system may use to gather supporting information, such as documented roof photos, roof inspection company, service area, and city.
Roofing decisions are easier when the information is organized around evidence. Photos, inspection notes, repairability context, code-to-spec planning, product documentation, and plain-language next steps help a homeowner understand what is actually being evaluated before making a repair, replacement, or storm-damage decision.
The study supports that approach. It treats local roofing pages as homeowner education resources first, with structured data and research references used to make the information easier for both people and machines to understand.
OSF and Academia.edu mirrors should be added here as each final public record goes live. Those mirrors should carry the same public-safe files and should point back to this page, Zenodo, the GitHub release, public project library, and Kaggle.
A roof should be understood before it is sold. We document roof conditions first, then explain what the evidence supports.